Overview

Dataset statistics

Number of variables9
Number of observations1030
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)1.1%
Total size in memory72.5 KiB
Average record size in memory72.1 B

Variable types

Numeric9

Alerts

Dataset has 11 (1.1%) duplicate rowsDuplicates
water is highly overall correlated with superplasticizerHigh correlation
superplasticizer is highly overall correlated with waterHigh correlation
age is highly overall correlated with concrete_compressive_strengthHigh correlation
concrete_compressive_strength is highly overall correlated with ageHigh correlation
blast_furnace_slag has 471 (45.7%) zerosZeros
fly_ash has 566 (55.0%) zerosZeros
superplasticizer has 379 (36.8%) zerosZeros

Reproduction

Analysis started2023-06-02 02:08:39.726092
Analysis finished2023-06-02 02:08:55.934910
Duration16.21 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

cement
Real number (ℝ)

Distinct278
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.16786
Minimum102
Maximum540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:56.086824image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum102
5-th percentile143.745
Q1192.375
median272.9
Q3350
95-th percentile480
Maximum540
Range438
Interquartile range (IQR)157.625

Descriptive statistics

Standard deviation104.50636
Coefficient of variation (CV)0.37168673
Kurtosis-0.52065228
Mean281.16786
Median Absolute Deviation (MAD)79.4
Skewness0.50948118
Sum289602.9
Variance10921.58
MonotonicityNot monotonic
2023-06-02T03:08:56.276920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362.6 20
 
1.9%
425 20
 
1.9%
251.4 15
 
1.5%
310 14
 
1.4%
446 14
 
1.4%
331 13
 
1.3%
475 13
 
1.3%
250 13
 
1.3%
349 12
 
1.2%
387 12
 
1.2%
Other values (268) 884
85.8%
ValueCountFrequency (%)
102 4
0.4%
108.3 4
0.4%
116 4
0.4%
122.6 4
0.4%
132 2
 
0.2%
133 5
0.5%
133.1 1
 
0.1%
134.7 1
 
0.1%
135 2
 
0.2%
135.7 2
 
0.2%
ValueCountFrequency (%)
540 9
0.9%
531.3 5
0.5%
528 1
 
0.1%
525 7
0.7%
522 2
 
0.2%
520 2
 
0.2%
516 2
 
0.2%
505 1
 
0.1%
500.1 1
 
0.1%
500 10
1.0%

blast_furnace_slag
Real number (ℝ)

Distinct185
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.895825
Minimum0
Maximum359.4
Zeros471
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:56.488986image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median22
Q3142.95
95-th percentile236
Maximum359.4
Range359.4
Interquartile range (IQR)142.95

Descriptive statistics

Standard deviation86.279342
Coefficient of variation (CV)1.1675807
Kurtosis-0.50817548
Mean73.895825
Median Absolute Deviation (MAD)22
Skewness0.8007169
Sum76112.7
Variance7444.1248
MonotonicityNot monotonic
2023-06-02T03:08:56.696167image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 471
45.7%
189 30
 
2.9%
106.3 20
 
1.9%
24 14
 
1.4%
20 12
 
1.2%
145 11
 
1.1%
98.1 10
 
1.0%
19 10
 
1.0%
26 8
 
0.8%
22 8
 
0.8%
Other values (175) 436
42.3%
ValueCountFrequency (%)
0 471
45.7%
11 4
 
0.4%
13.6 5
 
0.5%
15 5
 
0.5%
17.2 1
 
0.1%
17.5 1
 
0.1%
17.6 1
 
0.1%
19 10
 
1.0%
20 12
 
1.2%
22 8
 
0.8%
ValueCountFrequency (%)
359.4 2
 
0.2%
342.1 2
 
0.2%
316.1 2
 
0.2%
305.3 4
0.4%
290.2 2
 
0.2%
288 4
0.4%
282.8 4
0.4%
272.8 2
 
0.2%
262.2 5
0.5%
260 1
 
0.1%

fly_ash
Real number (ℝ)

Distinct156
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.18835
Minimum0
Maximum200.1
Zeros566
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:56.908475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3118.3
95-th percentile167
Maximum200.1
Range200.1
Interquartile range (IQR)118.3

Descriptive statistics

Standard deviation63.997004
Coefficient of variation (CV)1.1810104
Kurtosis-1.3287464
Mean54.18835
Median Absolute Deviation (MAD)0
Skewness0.53735391
Sum55814
Variance4095.6165
MonotonicityNot monotonic
2023-06-02T03:08:57.150598image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 566
55.0%
118.3 20
 
1.9%
141 16
 
1.6%
24.5 15
 
1.5%
79 14
 
1.4%
94 13
 
1.3%
100.4 11
 
1.1%
125.2 10
 
1.0%
95.7 10
 
1.0%
98.8 10
 
1.0%
Other values (146) 345
33.5%
ValueCountFrequency (%)
0 566
55.0%
24.5 15
 
1.5%
59 1
 
0.1%
60 1
 
0.1%
71 1
 
0.1%
71.5 1
 
0.1%
75.6 1
 
0.1%
76 1
 
0.1%
77 2
 
0.2%
78 2
 
0.2%
ValueCountFrequency (%)
200.1 1
 
0.1%
200 1
 
0.1%
195 3
0.3%
194.9 1
 
0.1%
194 1
 
0.1%
193 1
 
0.1%
190 1
 
0.1%
187 1
 
0.1%
185.3 1
 
0.1%
185 2
0.2%

water
Real number (ℝ)

Distinct195
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181.56728
Minimum121.8
Maximum247
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:57.355618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum121.8
5-th percentile146.1
Q1164.9
median185
Q3192
95-th percentile228
Maximum247
Range125.2
Interquartile range (IQR)27.1

Descriptive statistics

Standard deviation21.354219
Coefficient of variation (CV)0.1176105
Kurtosis0.12208167
Mean181.56728
Median Absolute Deviation (MAD)13
Skewness0.074628384
Sum187014.3
Variance456.00265
MonotonicityNot monotonic
2023-06-02T03:08:57.581558image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192 118
 
11.5%
228 54
 
5.2%
185.7 46
 
4.5%
203.5 36
 
3.5%
186 28
 
2.7%
164.9 20
 
1.9%
162 20
 
1.9%
185 15
 
1.5%
153.5 15
 
1.5%
200 14
 
1.4%
Other values (185) 664
64.5%
ValueCountFrequency (%)
121.8 5
0.5%
126.6 5
0.5%
127 1
 
0.1%
127.3 1
 
0.1%
137.8 5
0.5%
140 1
 
0.1%
140.8 5
0.5%
141.8 5
0.5%
142 1
 
0.1%
143.3 5
0.5%
ValueCountFrequency (%)
247 1
 
0.1%
246.9 1
 
0.1%
237 1
 
0.1%
236.7 1
 
0.1%
228 54
5.2%
221.4 1
 
0.1%
221 2
 
0.2%
220.1 1
 
0.1%
220 2
 
0.2%
219.7 1
 
0.1%

superplasticizer
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2046602
Minimum0
Maximum32.2
Zeros379
Zeros (%)36.8%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:57.796421image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.4
Q310.2
95-th percentile16.055
Maximum32.2
Range32.2
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation5.9738414
Coefficient of variation (CV)0.96279912
Kurtosis1.411269
Mean6.2046602
Median Absolute Deviation (MAD)5.3
Skewness0.90720257
Sum6390.8
Variance35.686781
MonotonicityNot monotonic
2023-06-02T03:08:58.005304image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 379
36.8%
11.6 37
 
3.6%
8 27
 
2.6%
7 19
 
1.8%
6 17
 
1.7%
9.9 16
 
1.6%
8.9 16
 
1.6%
7.8 16
 
1.6%
9 16
 
1.6%
10 15
 
1.5%
Other values (101) 472
45.8%
ValueCountFrequency (%)
0 379
36.8%
1.7 4
 
0.4%
1.9 1
 
0.1%
2 1
 
0.1%
2.2 1
 
0.1%
2.5 2
 
0.2%
3 6
 
0.6%
3.1 1
 
0.1%
3.4 3
 
0.3%
3.6 5
 
0.5%
ValueCountFrequency (%)
32.2 5
0.5%
28.2 5
0.5%
23.4 5
0.5%
22.1 1
 
0.1%
22 6
0.6%
20.8 1
 
0.1%
20 1
 
0.1%
19 1
 
0.1%
18.8 1
 
0.1%
18.6 5
0.5%

coarse_aggregate
Real number (ℝ)

Distinct284
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean972.91893
Minimum801
Maximum1145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:58.384125image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum801
5-th percentile842
Q1932
median968
Q31029.4
95-th percentile1104
Maximum1145
Range344
Interquartile range (IQR)97.4

Descriptive statistics

Standard deviation77.753954
Coefficient of variation (CV)0.079918225
Kurtosis-0.5990161
Mean972.91893
Median Absolute Deviation (MAD)46.3
Skewness-0.040219745
Sum1002106.5
Variance6045.6774
MonotonicityNot monotonic
2023-06-02T03:08:58.585231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
932 57
 
5.5%
852.1 45
 
4.4%
944.7 30
 
2.9%
968 29
 
2.8%
1125 24
 
2.3%
1047 19
 
1.8%
967 19
 
1.8%
974 12
 
1.2%
942 12
 
1.2%
938 12
 
1.2%
Other values (274) 771
74.9%
ValueCountFrequency (%)
801 4
0.4%
801.1 1
 
0.1%
801.4 1
 
0.1%
811 2
0.2%
814 1
 
0.1%
814.1 1
 
0.1%
817.9 1
 
0.1%
818 1
 
0.1%
819 2
0.2%
819.2 1
 
0.1%
ValueCountFrequency (%)
1145 1
 
0.1%
1134.3 5
 
0.5%
1130 1
 
0.1%
1125 24
2.3%
1124.4 2
 
0.2%
1120 2
 
0.2%
1119 2
 
0.2%
1118.8 2
 
0.2%
1118 1
 
0.1%
1113 2
 
0.2%

fine_aggregate
Real number (ℝ)

Distinct302
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean773.58049
Minimum594
Maximum992.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:58.808231image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum594
5-th percentile613
Q1730.95
median779.5
Q3824
95-th percentile898.09
Maximum992.6
Range398.6
Interquartile range (IQR)93.05

Descriptive statistics

Standard deviation80.17598
Coefficient of variation (CV)0.10364271
Kurtosis-0.10217699
Mean773.58049
Median Absolute Deviation (MAD)45.5
Skewness-0.2530096
Sum796787.9
Variance6428.1878
MonotonicityNot monotonic
2023-06-02T03:08:59.015206image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
755.8 30
 
2.9%
594 30
 
2.9%
670 23
 
2.2%
613 22
 
2.1%
801 16
 
1.6%
746.6 15
 
1.5%
887.1 15
 
1.5%
712 14
 
1.4%
845 14
 
1.4%
750 12
 
1.2%
Other values (292) 839
81.5%
ValueCountFrequency (%)
594 30
2.9%
605 5
 
0.5%
611.8 5
 
0.5%
612 1
 
0.1%
613 22
2.1%
613.2 2
 
0.2%
614 1
 
0.1%
623 2
 
0.2%
630 5
 
0.5%
631 4
 
0.4%
ValueCountFrequency (%)
992.6 5
0.5%
945 4
0.4%
943.1 4
0.4%
942 4
0.4%
925.7 5
0.5%
905.9 5
0.5%
903.8 5
0.5%
903.6 5
0.5%
901.8 5
0.5%
900.9 5
0.5%

age
Real number (ℝ)

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.662136
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:59.192234image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median28
Q356
95-th percentile180
Maximum365
Range364
Interquartile range (IQR)49

Descriptive statistics

Standard deviation63.169912
Coefficient of variation (CV)1.38342
Kurtosis12.168989
Mean45.662136
Median Absolute Deviation (MAD)21
Skewness3.2691774
Sum47032
Variance3990.4377
MonotonicityNot monotonic
2023-06-02T03:08:59.330155image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
28 425
41.3%
3 134
 
13.0%
7 126
 
12.2%
56 91
 
8.8%
14 62
 
6.0%
90 54
 
5.2%
100 52
 
5.0%
180 26
 
2.5%
91 22
 
2.1%
365 14
 
1.4%
Other values (4) 24
 
2.3%
ValueCountFrequency (%)
1 2
 
0.2%
3 134
 
13.0%
7 126
 
12.2%
14 62
 
6.0%
28 425
41.3%
56 91
 
8.8%
90 54
 
5.2%
91 22
 
2.1%
100 52
 
5.0%
120 3
 
0.3%
ValueCountFrequency (%)
365 14
 
1.4%
360 6
 
0.6%
270 13
 
1.3%
180 26
 
2.5%
120 3
 
0.3%
100 52
 
5.0%
91 22
 
2.1%
90 54
 
5.2%
56 91
 
8.8%
28 425
41.3%
Distinct845
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.817961
Minimum2.33
Maximum82.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2023-06-02T03:08:59.524485image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum2.33
5-th percentile10.961
Q123.71
median34.445
Q346.135
95-th percentile66.802
Maximum82.6
Range80.27
Interquartile range (IQR)22.425

Descriptive statistics

Standard deviation16.705742
Coefficient of variation (CV)0.46640684
Kurtosis-0.31372486
Mean35.817961
Median Absolute Deviation (MAD)10.93
Skewness0.41697729
Sum36892.5
Variance279.08181
MonotonicityNot monotonic
2023-06-02T03:08:59.739350image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.4 6
 
0.6%
77.3 4
 
0.4%
79.3 4
 
0.4%
31.35 4
 
0.4%
71.3 4
 
0.4%
35.3 4
 
0.4%
23.52 4
 
0.4%
41.05 4
 
0.4%
44.28 3
 
0.3%
41.54 3
 
0.3%
Other values (835) 990
96.1%
ValueCountFrequency (%)
2.33 1
0.1%
3.32 1
0.1%
4.57 1
0.1%
4.78 1
0.1%
4.83 1
0.1%
4.9 1
0.1%
6.27 1
0.1%
6.28 1
0.1%
6.47 1
0.1%
6.81 1
0.1%
ValueCountFrequency (%)
82.6 1
 
0.1%
81.75 1
 
0.1%
80.2 1
 
0.1%
79.99 1
 
0.1%
79.4 1
 
0.1%
79.3 4
0.4%
78.8 1
 
0.1%
77.3 4
0.4%
76.8 1
 
0.1%
76.24 1
 
0.1%

Interactions

2023-06-02T03:08:53.718997image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:40.057902image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:41.866726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:43.563886image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:45.116517image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:46.663813image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:48.417028image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:50.203007image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:51.970000image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:53.903891image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:40.249204image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:42.160689image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:43.715801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:45.272495image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:46.820724image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:48.577937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:50.382905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:52.129920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:54.097780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:40.455086image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:42.334590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:43.889714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:45.444580image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:46.993627image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:48.771826image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:50.564801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:52.316799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:54.282676image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:40.657067image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:42.509491image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:44.058604image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:45.613702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:47.173524image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:48.993698image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:50.770683image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:52.529691image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:54.611487image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:40.855833image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:42.675396image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:44.223645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:45.772889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:47.341645image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:49.178594image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:50.974567image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:52.730562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:54.791383image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:41.033829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:42.840301image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:44.385623image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:45.933937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:47.551522image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:49.362489image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:51.157462image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:52.917455image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:54.964287image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:41.249793image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:43.014202image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:44.558618image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:46.105015image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:47.724425image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:49.575366image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:51.342356image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:53.104348image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:55.157175image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:41.453770image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:43.207092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:44.746714image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:46.304895image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:47.902324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:49.774252image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:51.575224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:53.317227image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:55.328079image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:41.655741image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:43.377992image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:44.931611image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:46.478920image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:48.243129image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:49.985131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:51.772111image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2023-06-02T03:08:53.498138image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2023-06-02T03:08:59.924242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
cementblast_furnace_slagfly_ashwatersuperplasticizercoarse_aggregatefine_aggregateageconcrete_compressive_strength
cement1.000-0.245-0.418-0.0940.038-0.145-0.1740.0050.478
blast_furnace_slag-0.2451.000-0.2540.0530.098-0.349-0.302-0.0180.164
fly_ash-0.418-0.2541.000-0.2830.4540.0580.0510.003-0.078
water-0.0940.053-0.2831.000-0.687-0.218-0.3460.091-0.308
superplasticizer0.0380.0980.454-0.6871.000-0.1990.168-0.0100.348
coarse_aggregate-0.145-0.3490.058-0.218-0.1991.000-0.100-0.045-0.184
fine_aggregate-0.174-0.3020.051-0.3460.168-0.1001.000-0.057-0.180
age0.005-0.0180.0030.091-0.010-0.045-0.0571.0000.596
concrete_compressive_strength0.4780.164-0.078-0.3080.348-0.184-0.1800.5961.000

Missing values

2023-06-02T03:08:55.563958image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-02T03:08:55.816799image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

cementblast_furnace_slagfly_ashwatersuperplasticizercoarse_aggregatefine_aggregateageconcrete_compressive_strength
0540.00.00.0162.02.51040.0676.02879.99
1540.00.00.0162.02.51055.0676.02861.89
2332.5142.50.0228.00.0932.0594.027040.27
3332.5142.50.0228.00.0932.0594.036541.05
4198.6132.40.0192.00.0978.4825.536044.30
5266.0114.00.0228.00.0932.0670.09047.03
6380.095.00.0228.00.0932.0594.036543.70
7380.095.00.0228.00.0932.0594.02836.45
8266.0114.00.0228.00.0932.0670.02845.85
9475.00.00.0228.00.0932.0594.02839.29
cementblast_furnace_slagfly_ashwatersuperplasticizercoarse_aggregatefine_aggregateageconcrete_compressive_strength
1020288.4121.00.0177.47.0907.9829.52842.14
1021298.20.0107.0209.711.1879.6744.22831.88
1022264.5111.086.5195.55.9832.6790.42841.54
1023159.8250.00.0168.412.21049.3688.22839.46
1024166.0259.70.0183.212.7858.8826.82837.92
1025276.4116.090.3179.68.9870.1768.32844.28
1026322.20.0115.6196.010.4817.9813.42831.18
1027148.5139.4108.6192.76.1892.4780.02823.70
1028159.1186.70.0175.611.3989.6788.92832.77
1029260.9100.578.3200.68.6864.5761.52832.40

Duplicate rows

Most frequently occurring

cementblast_furnace_slagfly_ashwatersuperplasticizercoarse_aggregatefine_aggregateageconcrete_compressive_strength# duplicates
1362.6189.00.0164.911.6944.7755.8335.304
3362.6189.00.0164.911.6944.7755.82871.304
4362.6189.00.0164.911.6944.7755.85677.304
5362.6189.00.0164.911.6944.7755.89179.304
2362.6189.00.0164.911.6944.7755.8755.903
6425.0106.30.0153.516.5852.1887.1333.403
7425.0106.30.0153.516.5852.1887.1749.203
8425.0106.30.0153.516.5852.1887.12860.293
9425.0106.30.0153.516.5852.1887.15664.303
10425.0106.30.0153.516.5852.1887.19165.203